1. Field of the Invention
The present invention generally concerns improved techniques for forecasting the values of variables, such as the price of a share of stock or a commodity. More specifically, the invention is directed to improved combination forecasting by using clusterization.
2. Description of the Related Art
Forecasting Contests
A number of forecasting contests have been conducted in the past. Such contests range from various wagering events, such as Superbowl pools, to various financial forecasting contests. Typically, such conventional contests seek to identify the best predictor for the outcome of a single event. For example, the Investorsforecast website allows participants to predict where the Dow Jones Industrial Average (DJIA) will be and what the prices of certain stocks will be at the end of next week. The person submitting the most accurate prediction for the DJIA and the person submitting the most accurate prediction for an individual stock are each given a fixed monetary award, such as $300. Other contests in the financial arena typically allow participants to invest an imaginary amount of money, with the winner being the person whose portfolio is the largest at the end of the contest. One example of such a contest can be seen at the Fantasystockmarket website.
However, the present inventors have discovered that such conventional contests are inadequate in the following respects. First, the rankings generated by such contests typically do not provide useful information for truly identifying the best forecasters. This is a particularly significant shortcoming with respect to financial and economic forecasting, in which it is very useful for third parties to have that information. In addition, these conventional contests often reward short-term or single-event thinking, and such qualities may not be the most desirable in many cases. Finally, partly because of such short-term and single-event thinking, partly because of the specific events for which predictions are solicited in such conventional contests, and partly because of the manner in which such conventional contests are typically structured, the utility of the data produced by such conventional contests for purposes such as-combination forecasting often is sub-optimal.
In the financial and economic arenas, the result is that traditionally there has been insufficient data upon which investors could rely in order to select investment advisors. As a result, many investors are left to select advisors based largely on arbitrary criteria or, in the best case, to rely on recommendations from friends. At the same time, many actual and potential investment advisors who are very capable at reading the market conventionally have had very little opportunity to demonstrate their expertise to the public, and thereby attract new clients. Similar concerns exist for other financial and economic experts who wish to demonstrate their expertise or the validity of their prediction techniques.
What is needed therefore, is a contest in which the rankings and/or rewards are tied more closely to the forecasting characteristics that are most desirable and that yields a large database of information which can serve as the basis for comparing the predictions of different forecasters. It is also desirable that the contest provide data that are statistically significant and can provide the basis for a wide variety of combination forecasts and other statistical analyses as well as being highly useful for marketing purposes.
Prediction Input
In conventional forecasting contests, participants typically submit their predictions by writing, typing or speaking their predictions. Most frequently, such predictions consist of a numerical estimate of what the value of the predicted variable will be at a specified point in time. Thus, for instance, in the Investorsforecast website contest mentioned above, participants type in the values of their estimates and then submit those estimates by clicking a button on the website.
While such prediction submission techniques are adequate for their intended purpose, they suffer from many shortcomings. The following examples of such shortcomings have been identified by the present inventors.
First, such conventional prediction submission techniques frequently are not very intuitive from the participant""s point of view. In particular, they often require the participants to digest a significant amount of information in order to translate their rough feelings about the way the prediction variable is likely to move into a hard number. This is a significant disadvantage for those participants who are very intuitive oriented. Moreover, to the extent such persons are prone to errors in processing such data when converting their rough perceptions into a hard number, their submitted predictions may vary from what they actually believe about the subject variable.
Second, having to enter numerical estimates for each prediction variable can be cumbersome and time-consuming. This may have the effect of limiting the number of variables for which participants are willing to submit predictions.
While other prediction submission techniques have been utilized, they typically have had very limited applicability. For example, the Cyberskipper website permits participants to compete in predicting certain sports-related events. One of the prediction submission techniques utilized by this site is to display a grid of possible events. The participants can then click on a cell within the grid to designate their prediction that a particular event will occur. Thus, a different grid is displayed for each baseball game, with each row of the grid corresponding to a different baseball player and each column corresponding to a different event (e.g., xe2x80x9crunsxe2x80x9d, xe2x80x9chitsxe2x80x9d, xe2x80x9chome runxe2x80x9d). If a participant believes that a certain player will get a home run in a game, he simply clicks on the appropriate cell to enter that prediction. As can be readily appreciated, this technique generally is limited to predicting binary events (i.e., will/will-not occur). In many cases, this deficiency will limit the applicability of such techniques to collection of very coarse predictions.
What is needed, therefore, is a more efficient and intuitive way to enter or submit prediction data that is applicable across a wide range of prediction events and that can permit participants to submit predictions with more specificity than has been available with conventional techniques.
Provision of On-line Resources
Use of the Internet has become more and more common over the past few years. Similarly, the number of websites on the Internet has grown exponentially and is expected to continue to grow at a fast pace. As a result, the amount of information available on the Internet can be staggering. However, there is often little done to insure that the information provided to end users is the most relevant to those users.
A typical website might contain advertising, as well as a certain amount of content. Both types of information are typically controlled exclusively by the owner of the website, possibly based loosely on some indications as to what visitors would like to see, or based on what advertisers might believe will be most effective. However, the present inventors question how good such strategies are at actually providing website visitors with the information that they actually want and, in any event, have concluded that the effectiveness of such conventional strategies must necessarily vary based on the website owner""s individual skill in gauging his audiences desires.
Accordingly, the present inventors have discovered that what is needed is a more systematic technique for providing appropriate resources to users over an electronic network, such as the Internet, that more accurately reflects the users"" desires.
Financial and Economic Forecasting
The American economy is made up of the simultaneous activities of hundreds of millions of participants, simultaneously buying and selling goods and services in the competitive economy. Probably the most famous market is the Stock Market for the buying and selling of corporate ownership. Each business day, millions of shares of stock are bought and sold at competitive prices. Prices set by the competitive market change as people obtain different information regarding the availability and demand for goods, services, and financial assets. No individual knows all the market conditions in advance of trying to buy or sell. Knowing what prices will be in the future could allow market participants to change the amounts at which they would otherwise transact (e.g., if prices are expected to increase in the near future, knowledgeable sellers might withhold inventory from the market place).
Almost as long as there have been measurements of economic data, people have attempted to formulate forecasts of prices and economic activity by using a variety of techniques. During the past fifty years, several distinct methodologies for producing economic forecasts have been explored. Some of the most important include large-scale econometric systems, time series methods, computationally intensive techniques, opinion polling, and combination methods.
Economists, mathematicians, and forecasters have spent over a century attempting to specify increasingly complex mathematical and statistical models, which, some believe, could allow accurate forecasting to take place. Beginning with economic and behavioral theory, mathematical equations representing the interactions of different variables with each other are hypothesized. Then, using a sophisticated set of econometric model identification techniques, specific numerical values for the equations"" parameters are calculated based on historical relationships and observed data. Examples of these models have included the DRI Model, the Wharton Model, and the UCLA Forecasting Project model. Such large multiple equation mathematical forecasting models of the economy are ever increasingly complex, modeling ever-finer levels of economic detail, but their very complexity often makes them inaccurate as forecasting tools.
Some of these models can be used with fair accuracy to provide xe2x80x9cwhat ifxe2x80x9d simulations for the economy, simulations beginning from a specific initial set of economic measurements and then computing the likely economic impact from various policy changes (e.g. tax cuts, military spending). However, to the extent that the starting values are not precisely measured, or that there are even ever-so-slight errors in the mathematical equations, the resulting forecasts can display extraordinary deviation from the values that eventually are observed in the economy. These problems are made worse if, for any reason, historical economic data were generated by a different set of relationships than are now found in the economy. In this regard, one wag observed that these models are so accurate, economists have successfully predicted 14 of the last 3 recessions. Even so, these large-scale economic forecasting models remain the xe2x80x9cgold standardxe2x80x9d for economic forecasting, and millions of dollars are spent each year to purchase forecasts from such systems.
Approximately thirty years ago, a group of econometricians, predominantly of British origin, began to develop alternative economic prediction methods. Foremost, single equation models using xe2x80x9ctime seriesxe2x80x9d techniques popular in engineering applications were found to out-predict the large multiple equation economic models. The development of straightforward computer programs implementing these forecasting techniques allowed for the rapid development of these single equation forecasting models. Numerous economic variables were found to be reasonably predictable using such techniques. These techniques have continued to advance with the development of more complicated techniques (known by acronyms such as xe2x80x9cARCHxe2x80x9d and xe2x80x9cGARCHxe2x80x9d). However, these forecasting techniques are viewed with some suspicion by-many economists and forecasters because they lead to models developed using empirical criteria, not models specified as the logical result of economic theory. Even so, single equation forecasting methods are among the most valuable tools used by technical and quantitative market analysts, and are widely applied by Wall Street xe2x80x9cRocket Scientistsxe2x80x9d and many practicing business forecasters.
Another set of xe2x80x9cRocket Sciencexe2x80x9d tools has become popular during the 1990s, the xe2x80x9ccomputationally intensivexe2x80x9d forecasting tools. Using massive computerized databases, mathematical search algorithms are employed to find xe2x80x9cblack boxesxe2x80x9d for forecasting. Such techniques include xe2x80x9cneural networksxe2x80x9d, large systems of empirically based equations with parameters that evolve over time. Neural networks appear to be used, for example, in creating the forecasts produced by the Forecasts website Ideally, neural networks learn from their mistakes and self correct. Although neural networks are the foundation of numerous automated trading and arbitrage systems on Wall Street, in practice they sometimes xe2x80x9clearnxe2x80x9d too slowly and converge on very localized forecasting rules, which do not generalize well.
In addition, public opinion polls and surveys have been used to forecast xe2x80x9cconsumer sentimentxe2x80x9d measures and to gather data on peoples"" consumption patterns. To some extent mirroring the data collection methods used by the government to estimate its official economic measures, these have demonstrated some ability to provide accurate forecasts of what upcoming government statistical releases will say. For instance, the University of Michigan Center for Social Research is identified with its surveyed Index of Consumer Sentiment. Other major public opinion polls also routinely include questions regarding economic conditions.
The final category of forecasts, so-called xe2x80x9cconsensus forecastsxe2x80x9d, is similar to opinion-poll surveys but with a key difference. In public opinion polls, random populations are sampled. In creating a consensus forecast, polls and surveys of economic and financial forecasters (and, sometimes, published forecasts) are conducted. Typically, the median value across participants is the consensus forecast. These surveys have proven to be quite good, generally outperforming over time the individual forecasters who are included in the panel underlying the consensus forecast. Consensus forecasts are regularly conducted for corporate earnings, money supply and interest rates, and key macroeconomic variables. For example, both IBES and First Call survey stock analysts to identify expected corporate earnings. MMS surveys bank economists to estimate the money supply figures on the upcoming Federal Reserve H-6 reports. Blue Chip Economic Indicators was perhaps the first service providing median and average forecasts from a group of forecasters for general economic variables. The National Association of Business Economists Forecast Survey provides at least quarterly reports on what its membership anticipates for certain general economic variables. The Federal Reserve conducts similar surveys of about 30 economic forecasters with results published regularly in the financial press.
Consensus forecasts are an example of a broader, but relatively infrequently applied category of xe2x80x9ccombination forecastsxe2x80x9d. Combination forecasts are forecasts created from a group of underlying forecasts. Approximately twenty-five years ago, combining forecasts was an active area of econometric research and many theoretical problems were solved, including sophisticated mathematical procedures for determining optimally changing weights for the combinations. Although the consensus forecast median is a combination forecast, median forecasts usually are not the best combination forecasts, given the available data. However, they are xe2x80x9cpretty goodxe2x80x9d combination forecasts, and can be easily calculated.
The consensus forecasts require no historical information about either predictions or accuracy. More sophisticated forecast combinations require a historical track record for each forecast to be included in the combination. Once this track record is available, the forecasts can be analyzed into optimal combinations much like investments are combined into an optimal portfolio.
While consensus forecasting is alive and well, it appears that the broader optimal forecast combination literature has been abandoned or forgotten except, perhaps, in a few academic strongholds. This is not surprising. At the time these theoretical combination techniques were being developed, the efficient market hypothesis was in its prime and stock market forecasts were viewed with great suspicion, if they were considered at all, by academics. Economic forecasts were generally produced on a monthly basis at best, and more often on a quarterly basis. Because virtually all computation was still done on cumbersome mainframe systems, often as overnight batch computation jobs, forecasts were expensive to obtain. Even if a large number of forecasts were available, the optimal combinations could have required more computing power than was readily available to users, just as the Markowitz portfolio problems were generally intractable in practice.
Consequently, the lesson that seemed to be learned from the forecasting combination literature is that people get more accurate predictions if they somehow take an average of forecasts. Hence, demand grew for consensus forecasts based on simple surveys of forecasters, but more advanced combinations were not widely used due to cost, data constraints, and computational complexity. Like many technologies, the optimal forecast combination techniques were developed before the infrastructure was available to allow for their effective implementation.
In addition, combination forecasting can be difficult to implement for a large forecasting panel over a significant period of time, largely because the makeup of the forecasting panel varies over time and because the frequency of participation by the various members of the forecasting panel cannot be adequately controlled.
Still further, in certain cases there may be insufficient forecaster participation to permit a combination forecast of sufficient accuracy. Also, even if an accurate combination forecast is generated for a variable, it may be difficult to say with any certainty what was the relative importance of various factors arriving at the forecast.
Thus, what is needed is a more accurate forecasting methodology that overcomes the above shortcomings in the prior art.
Utilization of Banner Ad Click-through Information
Many conventional websites include banner advertisements which also function as hyperlinks to the advertiser""s website. Thus, if a website visitor is sufficiently interested by the advertisement, he can simply click on the advertisement to retrieve the advertiser""s webpage and obtain more information about the particular product or service. Use of such banner advertisements can provide advertising revenue for the displaying website and additional exposure for the advertising company.
In order to better target their advertising efforts, such advertisers might keep track of how many visitors to their site resulted from click-throughs for each of the various banner ads they have posted on others"" websites. However, the present inventors have discovered that banner ad click-through information can be used in a wide variety of additional applications, such as further increasing the efficiency of advertisers"" marketing efforts, predicting certain events, and others.
The present invention addresses the foregoing problems by providing a number of different inventive features which can be implemented individually or in any of a wide variety of combinations. These inventive features generally can be grouped according to the following categories.
Forecasting Contest
The present invention provides forecasting contests that include features directed to better ranking of the participants and/or that result in a better database of prediction data.
Thus, in one aspect, the invention is directed to conducting a contest that produces forecasting data for predesignated variables whose values change over time. Initially, participant registrations are accepted, and the participants are permitted to submit predictions of values, projected at plural different time points, for at least one of several predesignated variables. For example, an individual participant might elect to predict what the exchange rate between the U.S. Dollar and the Japanese Yen will be at the end of next week and at the end of the year. Then, the participants receive an overall ranking based on their relative accuracies (e.g., percentile rankings) in individual prediction events.
By ranking individuals based on their relative accuracies in individual prediction events, a contest conducted according to this aspect of the invention permits an overall ranking within a group of participants even though the participants in the group might be predicting different combinations of variables or might be predicting for different time horizons. At the same time, ranking based on performance in a number of different prediction events often can provide more meaningful rankings, for example, by eliminating many of the incentives to engage in strategies that may occasionally provide high rankings in individual prediction events. For instance, in conventional contests that rank based on accuracy in individual prediction events and recognition is given only to the top performers, a participant might have a strategic incentive to predict relatively unlikely values rather than values that he actually expects to occur so that occasionally he will be correct and will be listed as a top forecaster, rather than always ranking near the middle.
In another aspect, the invention is directed to conducting a contest that produces forecasting data for predesignated variables whose values change over time. Participant registrations are accepted, but in this aspect of the invention registration by a participant requires providing information regarding demographic characteristics of the participant. Participants are then permitted to submit predictions of values, projected at plural different time points, for at least one of certain predesignated variables. Finally, the participants are ranked based on their track records over a predefined period of time. In this aspect of the invention, the predesignated variables include economic and/or financial variables, and participants are rewarded for updating their predictions as early as possible.
By requiring demographic information as a condition to registration, this aspect of the invention can often create a more useful database of prediction data for purposes such as combination forecasting. Also, rewarding participants for updating their predictions as early as possible can provide a fuller, more complete and more continuous database. Finally, as noted above, by ranking based on track record over a pre-determined period of time, single-event strategies often can be largely eliminated.
In another aspect, the invention is directed to conducting a contest that produces forecasting data for predesignated variables whose values change over time. Participant registrations are accepted, with participant registration including providing information regarding personal characteristics of the participant. The participants are permitted to submit predictions of values, projected at plural different time points, for at least one of certain predesignated variables, including economic and/or financial variables. Then, the participants are ranked based on their track records over a predefined period of time. This ranking includes: (1) determining, for each participant and for each of plural prediction events in which the participant competed, a percentile rank in comparison to other participants who competed in the prediction event; (2) combining the percentile ranks for each participant to produce a raw score for the participant; and (3) ranking the participants based on the raw score for each participant.
The ranking technique utilized in this aspect of the invention can be systematic and automatically implemented, while maintaining the above-described advantages of providing an overall ranking based on relative accuracies in individual prediction events.
In a still further aspect, the invention is directed to conducting a contest that produces forecasting data for predesignated variables whose values change over time. Participant registrations are accepted, and the participants are permitted to submit predictions of values, projected at plural different time points, for at least one of certain predesignated variables. The participants then receive an overall ranking based on their track record over a pre-defined period of time and based on consistency of their accuracies in individual prediction events.
By basing overall ranking on accuracy consistency in individual prediction events, as well as on track record, this aspect of the invention can often provide better ranking information than conventional ranking techniques permit. For example, in the investment arena an important quality in judging the merit of an investment advisor will often be consistency, as inconsistency typically translates directly into higher risk. Thus, by ranking based on a combination of accuracy and consistency, this aspect of the present invention can often provide a ranking that is typically more meaningful to third parties, such as investors.
In a still further aspect, the invention is directed to conducting a contest that produces forecasting data for predesignated variables whose values change over time. Participant registrations are accepted, and the participants are permitted to submit predictions in plural different prediction events, each prediction event having a closing time point by which final predictions must be submitted. Then, an overall ranking of the participants is determined based on the participants"" track records in the prediction events over a pre-defined period of time and based on how soon their final predictions were made before the closing time points.
By basing the overall ranking on how soon the participants"" final predictions were made before certain closing time points, as described above, this aspect of the invention often encourages earlier predictions and more frequent prediction updates, thereby providing a more complete database of prediction data. At the same time, participants are rewarded for discovering and/or incorporating new information into their predictions at the earliest possible time, with the result that the both quality of the prediction data and the quality of the rankings are likely enhanced.
In a still further aspect, the invention is directed to conducting a contest that produces forecasting data for predesignated variables whose values change over time. Participant registrations are accepted, and the participants are permitted to submit predictions of values, projected at plural different time points, for at least one of certain predesignated variables. The participants also are permitted to submit estimates of their own uncertainty regarding their predictions.
By permitting participants to submit estimates of their own prediction uncertainty in the foregoing manner, participants often are encouraged to participate more frequently, even if they are somewhat less certain regarding their predictions. As a result, more data are collected. At the same time, the additional uncertainty data enhances the prediction data database, thus frequently permitting more accurate combination forecasts, more accurate determination of other statistical indicators, and even creation of additional statistical measures, all toward the end of more accurately gauging the sentiments of the forecasting panel.
Prediction Input
The invention also addresses the above-mentioned problems in the prior art by permitting users to enter predictions graphically.
Thus, in one aspect the invention is directed to facilitating the entry of prediction data. Initially, a graph is electronically displayed, the graph including a historical portion that includes historical values of the variable over time and also including a future portion. Then, a participant is permitted to designate a point on the future portion of the graph (e.g., by using an input device such as a mouse, a touch-sensitive display screen or the like) and the designated point is converted into a predicted value for the variable at a realization time.
In another aspect, the invention is directed to a method for entering prediction data for a variable. Initially, a participant causes a graph to be electronically displayed, the graph including a historical portion that includes historical values of the variable over time and also including a future portion. Next, the participant designates a point on the future portion of the graph, the position of the point corresponding to the predicted value for the variable at a particular realization time and also corresponding to the realization time itself. For instance, the horizontal position of the point might correspond to the realization time while the vertical position of the point corresponds to the predicted value. Finally, the participant enters the predicted value, such as by clicking on an xe2x80x9centerxe2x80x9d button.
By allowing a participant to see a graphical depiction of historical values for a prediction variable and then to enter a prediction value for the variable in the foregoing manner, the present invention can offer a more intuitive way to enter prediction values than has been available in the prior art techniques. In addition, the foregoing technique can permit a participant to observe and evaluate a significant amount of information at the same time that he is entering his prediction.
Additional features of the invention include: also displaying on the same graph historical values for other variables; providing the ability to display the historical data and/or the predicted value for the prediction variable with respect to a different independent variable than in the initial graph; displaying multiple variables on an initial graph in a first view (e.g., a time series view) and then permitting the participant to obtain a view that is a rotation of the first view (e.g., a cross-maturity comparison view); permitting the participant to numerically alter the prediction after it has been entered graphically; permitting the participant to alternatively bypass the graphical input altogether and instead enter the prediction numerically; permitting the participant to enter, in addition to his prediction, an estimate of his own uncertainty regarding his prediction; permitting the participant to graph only certain ranges specified by the participant; permitting the participant to change scales of the graph; permitting the participant to obtain graphs of arbitrarily requested mathematical transformations of historical and/or prediction data; permitting the participant to alter his predictions based on any of the foregoing different views, and even from within any or all of the different views; linking historical and/or current data, news, publications, etc. to the cursor position as it moves across the graph, so that such information is easily and conveniently available to the participant; and, lastly, matching the participant""s prediction(s) to different prediction models to find the closest model, and thereafter providing the participant with information regarding the model, such as the type of model, the implied assumptions in the participant""s prediction(s), and the amount of weight the participant is implicitly applying to different items or pieces of information that underlie the identified forecasting model.
Any or all of the foregoing features can be included in the prediction input techniques of the present invention. All enhance the basic prediction input technique described above by providing the participant with a wide variety of different types of data in any of a wide variety of different formats, thus permitting each individual participant to obtain the data that are most useful to him and to display such data in the format(s) that are most useful to him.
Community-selected Content
The present invention also addresses the above-described problems of providing the most useful content over an electronic network, such as the Internet. Generally speaking this problem is addressed in the present invention by providing a systematic technique for allowing users to participate in determining what content is most useful to them.
Thus, according to one aspect, the invention maintains a collection of resources that can be accessed by a participant over the electronic network (such as the Internet) at a given time and, typically upon request, provides such resources to the participant over the electronic network. Points are assigned to each resource based on participant access of the resource and the collection is modified based on the points assigned to each resource. For instance, a fixed number of points may be assigned to each resource when a participant accesses the resource and the resources having the worst overall rating based on assigned points may be removed from the collection. Alternatively, a resource may be moved from the initial collection and placed in a second collection when its number of points has reached a certain predetermined criterion (e.g., a fixed number or a fixed number within a set period of time).
By assigning points and modifying the collection in the foregoing manner, the present invention can provide a systematic and automatic technique for updating a collection of resources over an electronic network, such as the Internet. In a more particularized aspect of the invention, the number of points assigned to a resource when a participant accesses the resource is based upon the participation level of the participant. In this way, the participants who are most active on the network can have the greatest impact on the resource collection.
In another particularized aspect of the invention, each resource is assigned a score based on the points assigned to the resource, with points assigned more recently being weighted more heavily in determining the score than points assigned less recently. In this way, it can be possible to properly maintain the collection even in the presence of changing tastes or changing consumer needs.
In a further aspect, the invention is directed to providing information to participants over an electronic network by maintaining a collection of resources. Participants are permitted to rate the resources and points are assigned to each resource based on participant rating of the resource. The collection of resources is then modified based on assigned points for each resource.
In the foregoing manner, participants have the ability to directly assess the usefulness of any particular resource to them and these assessments are utilized to modify the collection. This can have the effect of making the resource collection even more responsive to the needs of the participants (or users) because, although a resource might initially appear to be valuable, upon closer inspection a user might find it to be inaccurate, poorly organized or lacking for any other reason. Thus, allowing participant ratings and the utilization of those ratings in the foregoing manner often will account for such problems.
In a still further aspect, the invention is directed to providing information to participants over an electronic network by maintaining a collection of resources. Participants are permitted to both access and rate the resources, with points assigned to each resource based on such ratings and access. The collection of resources is then modified based on total points for each resource.
By combining point assignments based on both ratings and access, this aspect of the invention often typically can provide all of the benefits described above.
Combination Forecasting Using Clusterization
The present invention addresses the problems with attempting to use combination forecasting in certain cases (such as where membership of the forecasting panel is inconsistent) by using clusterization techniques.
Thus, in one aspect, the invention is directed to providing combination forecasts using predictions obtained from a group of forecasters. The forecasters are first divided into a number of pre-defined clusters, which typically will have been formed using statistical clustering techniques. In particular, clusters of forecasters can be formed based on similarities of the forecasters"" predictions. Then, statistical data are calculated for each pre-defined cluster (e.g., measures of central tendency and dispersion). Finally, the statistical data for all the pre-defined clusters are combined so as to obtain a combination forecast.
By utilizing clustering in the foregoing manner, the present invention often can avoid the difficulties of inconsistent forecaster participation. For instance, by utilizing cluster statistics, it often will much less significant whether or not any particular individual submits a forecast for a given prediction event.
The foregoing steps can be repeated for each new prediction event. For example, after an initial clustering with respect to a given prediction variable, each time it is desired to generate a new combination forecast for that prediction variable, the currently participating forecasters can be simply assigned to their previously identified clusters and then new cluster statistics can be determined and combined.
When generating the combination forecast, it is generally preferable to weight the central tendency for each cluster based on its dispersion measure (e.g., more tightly clustered predictions given more weight than less tightly clustered predictions) and/or based on the cluster""s previous prediction accuracy (e.g., clusters having historically better prediction accuracies are given more weight).
It is also preferable to periodically re-cluster the forecasters to obtain a new set of pre-defined clusters. This often will be desirable to take account of shifting demographics, attitudes, social climates, economic conditions, and similar matters.
More particularized aspects of the invention also include identifying an assignment formula for assigning each new forecaster to a pre-defined cluster based on personal characteristics of the new forecaster. This feature of the invention can permit additions of new forecasters in between re-clusterizations.
Forecasting Using Interpolation Modeling
The present invention also addresses the problems of predicting variables for which there is insufficient forecaster participation and parsing changes in the value of a variable to determine the relative impact of various factors on the change.
Thus, in one aspect, the invention is directed to predicting a value of a target variable based on predictions of other variables. This aspect of the invention involves obtaining historical values for the target variable at each of several time points and obtaining previously predicted values and currently predicted values for each of several predictor variables, the predictor variables being different from the target variable. Values are assigned to parameters of a forecasting model to obtain the best fit of the previously predicted values for the predictor variables to the historical values for the target variable. Finally, a value of the target variable is predicted from the currently predicted values for at least a subset of the predictor variables using the forecasting model and the values assigned to the parameters of the forecasting model.
By using predictions of other variables in the foregoing manner, the present invention is often able to predict a value for a target variable for which there is insufficient forecaster participation. For example, there might be insufficient forecasters to produce a good combination forecast for the share price of a thinly traded stock. However, using predictions of other similar stocks in the foregoing manner, a fairly good forecast for the share price of such a stock often will still be possible.
Moreover, even when there is sufficient forecaster participation, the prediction for the target variable produced in the foregoing manner can be compared to realized values of the target variable and to other predictions of the target variable (such as a combination forecast) in order to sort out the influences of different factors. This advantage is often very helpful in assessing the impact of similar factors in the future. For example, calculating the difference between the value of the target variable predicted in the above manner and the actual value realized for the target variable typically will provide a measure of information that is specific to the target variable. Similarly, calculating the difference between the value of the target variable predicted in the foregoing manner and the value predicted for the target variable using a combination forecasting technique typically will provide an estimate of expected information that is specific to the target variable.
Pricing Derivative Instruments
The present invention also provides a novel technique for pricing derivative instruments by using forecast data.
Thus, in one aspect, the present invention is directed to pricing a derivative instrument whose value is dependent upon the value of an underlying asset at a future date. For each of a number of predetermined different prices, the value of a derivative instrument is calculated if the underlying asset were to be priced at that price on a future date. A number of individual forecasts of the value of the underlying asset on the future date are obtained. A probability is determined for each price, from the number of predetermined different prices of the underlying asset, as the proportion of individual forecasts that were closer to that price than to any other of the predetermined different prices. Finally, the derivative instrument is priced based on the values calculated for the derivative instrument above and based on the probabilities determined above. Preferably, the derivative instrument is priced as the sum, over the number of predetermined different prices, of the value identified above for the derivative instrument if the underlying asset were priced at a given price on the future date, times the probability determined above for that given price.
By virtue of the foregoing technique, a price can be determined for a derivative instrument, often without the need to assume a particular shape of the probability density function for the value of the underlying asset and without having to rely on historical variances, which are often poor indicators of future variances.
The foregoing technique can also be repeated for multiple time points within the period during which rights under the derivative instrument may be exercised. The resulting multiple different prices can then be combined, such as by taking a maximum of such prices, or in various other manners, to determine a final price for the derivative instrument.
Utilization of Banner Ad Click-through Information
The present invention provides the following novel techniques for utilizing banner ad click-through information to predict values of variables and to manage the display of banner ads.
In one aspect, the invention is directed to forecasting values for a variable by obtaining click-through data (e.g., click-through rates or changes in click-through rates) for website banner advertisements. Initially, a forecasting model is created for a variable (e.g., using a regression technique to create a linear or non-linear forecasting model), based on correlations of historical values of the click-through data with historical values of the variable. Then, the forecasting model is used to predict a future value of the variable.
In the foregoing manner, click-through data can often be used to predict a variable. For example, it may be possible to more accurately predict new housing starts in part based on the click-through rate for a particular mortgage advertisement.
In more particularized aspects of the invention, the website banner advertisements may be sorted into groups by categorizing them according to product/service advertised. Utilizing statistics for each such group may provide continuity while at the same time lessening the effects of changing advertisements. Thus, for example, new housing starts may be predicted based on the click-through rates for all mortgage advertisements.
In a further aspect, the invention is directed to displaying website banner advertisements. The displayed website banner advertisements are sorted into categories based on product/service sold. An individual click-through rate is determined for each website banner advertisement and an aggregate click-through rate is determined for each category. Then, which website banner advertisements are displayed is changed based on a comparison between information pertaining to the individual click-through rate for a selected website banner advertisement and information pertaining to the aggregate click-through rate for the category to which the selected website banner advertisement belongs.
The foregoing technique often can permit the display of more effective website banner advertisements. For example, if the click-through rate for a particular mortgage advertisement is significantly less than the click-through rate for all mortgage advertisements, that particular mortgage advertisement may need to be modified or replaced.
Comments Regarding Summary
The foregoing summary is intended merely to provide a quick understanding of the general nature of the present invention. A more complete understanding of the invention can only be obtained by reference to the following detailed description of the preferred embodiments in connection with the accompanying drawings.